Screen Time and Obsessive-Compulsive Disorder Among Children 9–10 Years Old: A Prospective Cohort Study
Bibliographic record
Abstract
PURPOSE: The aim of this study is to determine the prospective associations between baseline screen time and obsessive-compulsive disorder (OCD) at 2-year follow-up in a national (United States) cohort of 9- to 10-year-old children. METHODS: We analyzed prospective cohort data from the Adolescent Brain Cognitive Development study (n = 9,208). Logistic regression analyses were used to determine the associations between baseline self-reported screen time (exposure) and OCD, based on the Kiddie Schedule for Affective Disorders and Schizophrenia (outcome), at 2-year-follow-up, adjusting for race/ethnicity, sex, household income, parent education, family history of psychopathology, and study site, excluding participants with baseline OCD. RESULTS: The sample was 48.9% female and racially and ethnically diverse (43.5% non-White). Each additional hour of total screen time was prospectively associated with 1.05 higher odds of OCD at 2-year follow-up (95% confidence interval [CI] 1.01-1.09). For specific screen time modalities, each additional hour of playing video games (adjusted odds ratio 1.15, 95% CI 1.03-1.28) and watching videos (adjusted odds ratio 1.11, 95% CI 1.01-1.23) was associated with a subsequent OCD diagnosis. CONCLUSION: Video games and watching videos are prospectively associated with new-onset OCD in early adolescents. Future research should examine mechanisms linking these specific screen modalities to OCD development to inform future prevention and intervention efforts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".